


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a			:			Union[str, Any]												=					logging.get_logger(__name__)

a			:			Any												=					{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					'xmod'






					def __init__(					self			,							snake_case_=3_0_5_2_2			,							snake_case_=7_6_8			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_=3_0_7_2			,							snake_case_="gelu"			,							snake_case_=0.1			,							snake_case_=0.1			,							snake_case_=5_1_2			,							snake_case_=2			,							snake_case_=0.0_2			,							snake_case_=1e-1_2			,							snake_case_=1			,							snake_case_=0			,							snake_case_=2			,							snake_case_="absolute"			,							snake_case_=True			,							snake_case_=None			,							snake_case_=False			,							snake_case_=2			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=("en_XX",)			,							snake_case_=None			,							**snake_case_			,							) ->      List[Any]:







								'''simple docstring'''

								super().__init__(pad_token_id=snake_case_			,							bos_token_id=snake_case_			,							eos_token_id=snake_case_			,							**snake_case_    )

								__lowercase			       = vocab_size
								__lowercase			       = hidden_size
								__lowercase			       = num_hidden_layers
								__lowercase			       = num_attention_heads
								__lowercase			       = hidden_act
								__lowercase			       = intermediate_size
								__lowercase			       = hidden_dropout_prob
								__lowercase			       = attention_probs_dropout_prob
								__lowercase			       = max_position_embeddings
								__lowercase			       = type_vocab_size
								__lowercase			       = initializer_range
								__lowercase			       = layer_norm_eps
								__lowercase			       = position_embedding_type
								__lowercase			       = use_cache
								__lowercase			       = classifier_dropout
								__lowercase			       = pre_norm
								__lowercase			       = adapter_reduction_factor
								__lowercase			       = adapter_layer_norm
								__lowercase			       = adapter_reuse_layer_norm
								__lowercase			       = ln_before_adapter
								__lowercase			       = list(snake_case_    )
								__lowercase			       = default_language




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					@property
					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								if self.task == "multiple-choice":
											__lowercase			       = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											__lowercase			       = {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								    ]    )






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


